PMLE Practice Question: Collaborating Within and Across Teams to Manage Data and Models
A data science team uses Vertex AI Experiments to compare multiple model training runs. They want to capture and compare hyperparameters, metrics, and code versions for each run. Which TWO steps should they take?
⚠ Common exam trap
PMLE often tests the misconception that Cloud Logging or BigQuery export are part of the experiment tracking workflow, when in fact Vertex AI Experiments requires explicit SDK logging and manual code version parameterization.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Log hyperparameters and metrics using the Vertex AI SDK's experiment logging functions
Option C is correct because the Vertex AI SDK provides dedicated experiment logging functions (e.g., aiplatform.log_params() and aiplatform.log_metrics()) that record hyperparameters and metrics directly into a Vertex AI Experiment run, which is exactly what the team needs to capture and compare across runs. Option E is correct because integrating training code with Git and passing the commit hash as a run parameter ties each experiment run to a specific, reproducible code version, satisfying the requirement to capture code versions alongside hyperparameters and metrics. Option A is not appropriate because Cloud Logging captures log output, not structured experiment parameters or metrics for comparison in Vertex AI Experiments. Option B is unnecessary and less precise than using Git commit hashes, since manually linking Cloud Storage artifacts does not automatically associate code versions with runs. Option D is not required because Vertex AI Experiments already provides comparison capabilities natively, so exporting to BigQuery is an extra step not needed for the stated goal.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use Cloud Logging to capture all training outputs
Why it's wrong here
Cloud Logging records runtime output for diagnostics, not structured experiment parameters, metrics or code versions that Vertex AI Experiments compares. It is tempting because logging is the default place to inspect training behaviour, and would be correct when debugging errors or tracing execution, but it cannot populate experiment run comparisons.
- ✗
Store code versions in Cloud Storage and link them to experiments manually
Why it's wrong here
Vertex AI Experiments automatically logs code versions when training runs are submitted through the SDK, so manual Cloud Storage linking adds no tracked metadata and breaks run comparability. Manual linking suits offline artefacts outside Vertex, not runs already captured by the service.
- ✓
Log hyperparameters and metrics using the Vertex AI SDK's experiment logging functions
Why this is correct
The Vertex AI SDK's experiment logging functions (aiplatform.log_params and log_metrics) attach hyperparameters and metrics to a named experiment run, which is exactly what enables side-by-side comparison of runs in the Vertex AI Experiments console. Code versions are captured separately via Git.
- ✗
Export experiment data to BigQuery for comparison
Why it's wrong here
Exporting to BigQuery adds a separate analytics step and does not itself capture parameters, metrics or code versions within Vertex AI Experiments. It is tempting because BigQuery suits large-scale comparison and visualisation, and would be right when experiment data must be joined with warehouse datasets for custom reporting.
- ✓
Integrate the training code with Git and use the commit hash as a run parameter
Why this is correct
Vertex AI Experiments records arbitrary parameters, so passing the Git commit hash as a run parameter ties each run to an exact code revision. This satisfies the code-version tracking requirement, complementing SDK metric and hyperparameter logging for full run reproducibility.
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Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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